Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Ravikumar, Penugonda; Likhitha, Palla; Raj, Bathala Venus Vikranth; Kiran, R. Uday; Watanobe, Yutaka; Zettsu, Koji

doi:10.3390/electronics10121478

Cited by 14 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we show the results of conduceted experimentation. The proposed algorithm PFPM-C is evaluated against the state-of-art algorithms PFP-growth [1], PFP-growth++ [3], and PFECLAT [14]) in terms of runtime requirements, and memory consumption. We conducted experiments on various real-world dense databases, by varying both minSup and maxP RD thresholds.…”

Section: Resultsmentioning

confidence: 99%

“…Anirudh et al [10] also presented a distributed in-memory algorithm based on map-reduce and Spark environment. Ravi et al [14] proposed an ECLAT-based [22] to find periodicfrequent patterns in columnar databases. Tarun et al [5] described a CUDA-based GPU algorithm to find periodicfrequent patterns.…”

Section: Periodic-frequent Pattern Miningmentioning

confidence: 99%

“…The complete set of periodic-frequent patterns generated from Table 1 is shown in Table 2. Several algorithms [3], [9]- [14] were described in the literature to find the periodic-frequent patterns in a database. The basic approach used in these algorithms has always been the same.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

Veena,

Sreepada,

Kiran

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Periodic-frequent patterns are a vital class of regularities in a temporal database. Most previous studies followed the approach of finding these patterns by storing the temporal occurrence information of a pattern in a list. While this approach facilitates the existing algorithms to be practicable on sparse databases, it also makes them impracticable (or computationally expensive) on dense databases due to increased list sizes. A renowned concept in set theory is larger the set, the smaller its complement will be. Based on this conceptual fact, this paper explores the complements, redefines the periodic-frequent pattern and proposes an efficient depth-first search algorithm called PFPM-C, that finds all periodic-frequent patterns by storing only non-occurrence information of a pattern in a database. Experimental results on several databases demonstrate that our algorithm is efficient.INDEX TERMS data mining, pattern mining, periodic patterns, set compliments, temporal databases I. INTRODUCTION

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Periodic-frequent Pattern Miningmentioning

confidence: 99%

See 1 more Smart Citation

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

Veena,

Sreepada,

Kiran

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also a pattern-growth algorithm was proposed to find the complete set of periodic-frequent itemset. Ravikumar et al [47] have described an algorithm named PF-ECLAT, to efficiently discover periodic-frequent itemsets in a columnar temporal databases. Some other variations of the above models were also proposed [44], [48], and [49] to find periodic-frequent itemset.…”

Section: B Periodic-frequent Itemset Miningmentioning

confidence: 99%

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

et al. 2023

Self Cite

View full text Add to dashboard Cite

Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable.INDEX TERMS Columnar databases, stable periodic-frequent itemset, itemset mining. I. INTRODUCTIONDatabase systems play a crucial role in storing the big data generated by real-world applications. Depending on the layout used for storing the data, one can broadly classify the databases into two types: row databases and columnar databases. 1 Row databases are primarily based on ACID 2 properties and organize the data as records by keeping the data associated with a record next to each other in a storage device. The popular row databases include MySQL [1] and Postgres [2]. In contrast, columnar databases are based on BASE 3 properties and organize data into fields and storeThe associate editor coordinating the review of this manuscript and approving it for publication was Laura Celentano .1 Columnar and row databases are referred as vertical and horizontal databases, respectively.2 ACID is an acronym for Atomicity, Consistency, Isolation, and Duration.3 BASE is an acronym for Basically Available, Soft state, and Eventually consistent.

show abstract

“…Ravi et al [36], [37] proposed PF-ECLAT, an extension of the ECLAT algorithm, for mining PFPs in columnar temporal databases. It employs a list-based approach and utilizes pruning techniques to efficiently discover interesting patterns.…”

Section: B Periodic Frequent Pattern Miningmentioning

confidence: 99%

k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases

Likhitha,

Ravikumar,

Saxena

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Finding periodic-frequent patterns in temporal databases is a prominent data mining problem with bountiful applications. It involves discovering all patterns in a database that satisfy the user-specified minimum support (min_sup) and maximum periodicity (max_per) constraints. M in_sup controls the least number of transactions in which a pattern must appear in a database. M ax_per controls the maximum time interval within which a pattern must reappear in the database. The popular adoption of this task has been hindered by an open problem, which involves setting appropriate min_sup and max_per values for any given database. This paper addresses this open problem by proposing a solution to discover top-k periodicfrequent patterns in a temporal database. Top-k periodic-frequent patterns represent k periodic-frequent patterns with the lowest periodicity value in a database. An efficient depth-first search algorithm, Top-k Periodic-Frequent Pattern Miner (k-PFPMiner), which takes only k threshold as an input, was presented to find all desired patterns in a database. Experimental results on synthetic and real-world databases demonstrate that our algorithm is memory and runtime-efficient and also highly scalable.

show abstract

Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Cited by 14 publications

References 19 publications

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases

Contact Info

Product

Resources

About